Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders

E. Sgouritsa; D. Janzing; Jonas Martin Peters; B. Schölkopf

Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders

E. Sgouritsa, D. Janzing, Jonas Martin Peters, B. Schölkopf

4 Citationer (Scopus)

Abstract

We propose a kernel method to identify finite mixtures of nonparametric product distributions. It is based on a Hilbert space embedding of the joint distribution. The rank of the constructed tensor is equal to the number of mixture components. We present an algorithm to recover the components by partitioning the data points into clusters such that the variables are jointly conditionally independent given the cluster. This method can be used to identify finite confounders.

Originalsprog	Udefineret/Ukendt
Titel	Proceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI)
Antal sider	10
Publikationsdato	2013
Sider	556-565
Status	Udgivet - 2013
Udgivet eksternt	Ja

Citationsformater

Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. / Sgouritsa, E.; Janzing, D.; Peters, Jonas Martin et al.

Proceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI). 2013. s. 556-565.

Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › peer review

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